In 2019, there were nearly 8,500 terrorist attacks around the world, killing more than 20,000 people (Source: Global Terrorism Overview). In order to successfully mitigate and combat terrorism it is imperative to understand the complex geopolitial dynamics that enable terrorism and terrorist ideologies. This project aims to analyze what one can infer about terrorist ideologies from data about their attacks. If one can identify causal relationships between characteristics of terrorists, including their ideologies and group structure, and the types of attacks perpetrated by these terrorists, then perhaps we could create informed policy to predict and mitigate terrorism.
Before proceeding, it is necessary to define key terms. Terrorism is notoriously difficult to define, and definitions are largely disagreed upon throughout industry, academia, and government. We will use data from the Global Terrorism Database (GTD) as a basis for our analysis, and therefore will utilize the definitions of terrorism and terrorist attacks provided in the datasets codebook:
A terrorist attack is the threatened or actual use of illegal force and violence by a non-state actor to gain political, economic, religious, or social goals through fear coercion, or intimidation.
(Source: GTD Codebook)
It is worth analyzing this definition to gain a better understanding of what is and what is not terrorism for the purposes of this project. The codebook indicates that terrorist attacks must be intentional, that does not mean that the attack is carried out exactly as intended, but rather that there is an intended target, a method by which to inflict harm, and perhaps evidence of planning. Additionally, a terrorist attack must include violence, or immediate threat of violence. This includes violence against both people and property. Violence in the codebook is to mean intention to cause injury and/or irrevocable destruction/kinectic damage. It is worth noting that the perpetrators must be sub-national actors. The database does not include acts of state terrorism--including persons who are employed by the state and/or are acting on behalf of a state or nation. This criteria does not exclude state-sponsored attacks, but rather only attacks perpetrated by state actors.
As with the definition above, two of the following three criteria must be met for inclusion in the dataset: 1. The act must be aimed at attaining a political, economic, religious, or social goal. In terms of economic goals, the exclusive pursuit of profit does not satisfy this criterion. It must involve the pursuit of more profound, systemic economic change. 2. There must be evidence of an intention to coerce, intimidate, or convey some other message to a larger audience (or audiences) than the immediate victims. It is the act taken as a totality that is considered, irrespective if every individual involved in carrying out the act was aware of this intention. As long as any of the planners or decision-makers behind the attack intended to coerce, intimidate or publicize, the intentionality criterion is met. 3. The action must be outside the context of legitimate warfare activities. That is, the act must be outside the parameters permitted by international humanitarian law.
For additional explanation of these criteria, as well as examples, please see the GTD Codebook.
Thankfully, the most tedious part of the data science pipeline has been done for us. The researchers over at the National Consortium for the Study of Terorrism and Responses to Terrorism (START) have collected data on global terrorism incidents from 1970-2019 in their Global Terrorism Database. The database, informed by open-source media articles, contains more than 100 structured variables characterize each attack’s location, tactics and weapons, targets, perpetrators, casualties and consequences, and general information such as definitional criteria and links between coordinated attacks. Unstructured variables include summary descriptions of the attacks and more detailed information on the weapons used, and specific motives of the attackers. The GTD is accessible for individuals and organizations from the GTD website.
While the methodology for collecting data has evolved since the inception of the database in 2006, it is worth mentioning the hybrid workflow the GTD employs to collect, process, and publish data today. The process starts with a pool of more than two million open-source media reports published each day. The GTD team combines automated and human workflows, leveraging the strengths and mitigating the limitations of each, to produce rich and reliable data. On the automated side, GTD researchers leverage boolean filters of articles, natural language processing (NLP), deduplication of articles, location identification, clustering of similar articles, and machine learning (ML) models to identify relevancy of articles. After the automated process has gathered, filtered and labeled articles, a team of analysts triage the articles to assess source validity, apply inclusion criteria, and create narratives of single incidents from multiple sources. The incidents are then coded by smaller teams, with specific domain expertise.
After creating an indiviual-use account for the GTD, we download the dataset and import as a dataframe using pandas.
!pip -q install wordcloud gensim nltk pyLDAvis
import pandas as pd
import numpy as np
ERROR: After October 2020 you may experience errors when installing or updating packages. This is because pip will change the way that it resolves dependency conflicts.
We recommend you use --use-feature=2020-resolver to test your packages with the new resolver before it becomes the default.
pyldavis 3.3.1 requires numpy>=1.20.0, but you'll have numpy 1.19.2 which is incompatible.
gtd_full = pd.read_excel("globalterrorismdb_0221dist.xlsx")
gtd_full.head()
Perhaps the first thing to note is that this dataframe quite large to be manipulating in a Jupyter Notebok. It contains over 200,000 incidents and 135 columns, and takes up about 100MB of memory. A dataset of this size may not be considered "big data", but it warrants careful consideration of how we analyze the data to avoid long wait times and computational inefficiency. First we are going to "clean" the data, by taking a subset of the columns that we will be using for our analysis, then our dataframe will be easier to iterate over and operate on and easier to read. Let's start our analysis with some simple plots. First let's look at the number of terrorist incidents and casualties over time
#subset of columns we will be working with in this project
subset = ['iyear', 'imonth', 'iday', 'country', 'country_txt', 'region', 'region_txt', 'provstate', 'city',
'attacktype1', 'attacktype1_txt', 'targtype1','targtype1_txt', 'gname', 'motive',
'weaptype1', 'weaptype1_txt', 'nkill', 'nwound']
#drop all the columns that aren't in the subset list above
gtd = gtd_full[subset]
#export this dataframe as its own excel file so we can load it faster in the future
gtd.to_excel('gtd-mini.xlsx', index = False)
gtd.head()
gtd = pd.read_excel('gtd-mini.xlsx')
gtd.head()
| iyear | imonth | iday | country | country_txt | region | region_txt | provstate | city | attacktype1 | attacktype1_txt | targtype1 | targtype1_txt | gname | motive | weaptype1 | weaptype1_txt | nkill | nwound | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1970 | 7 | 2 | 58 | Dominican Republic | 2 | Central America & Caribbean | National | Santo Domingo | 1 | Assassination | 14 | Private Citizens & Property | MANO-D | NaN | 13 | Unknown | 1.0 | 0.0 |
| 1 | 1970 | 0 | 0 | 130 | Mexico | 1 | North America | Federal | Mexico city | 6 | Hostage Taking (Kidnapping) | 7 | Government (Diplomatic) | 23rd of September Communist League | NaN | 13 | Unknown | 0.0 | 0.0 |
| 2 | 1970 | 1 | 0 | 160 | Philippines | 5 | Southeast Asia | Tarlac | Unknown | 1 | Assassination | 10 | Journalists & Media | Unknown | NaN | 13 | Unknown | 1.0 | 0.0 |
| 3 | 1970 | 1 | 0 | 78 | Greece | 8 | Western Europe | Attica | Athens | 3 | Bombing/Explosion | 7 | Government (Diplomatic) | Unknown | NaN | 6 | Explosives | NaN | NaN |
| 4 | 1970 | 1 | 0 | 101 | Japan | 4 | East Asia | Fukouka | Fukouka | 7 | Facility/Infrastructure Attack | 7 | Government (Diplomatic) | Unknown | NaN | 8 | Incendiary | NaN | NaN |
The new dataframe is about one-sixth the size of the original, now 15.2MB. The original dataframe contained columns that described sources, validity, detailed text descriptions, and more. With the exception of two unstructured text columns, we have only kept structured data describing the incidents. We breakdown columns and what they are recording in the table below:
| Column Name | Variable Name | Data Type | Description |
|---|---|---|---|
| iyear | Year | interval | This field contains the year in which the incident occurred. In the case of incident(s) occurring over an extended period, the field will record the year when the incident was initiated. |
| imonth | Month | categorical | This field contains the number of the month in which the incident occurred. In the case of incident(s) occurring over an extended period, the field will record the month when the incident was initiated. |
| iday | Day | interval | This field contains the numeric day of the month on which the incident occurred. In the case of incident(s) occurring over an extended period, the field will record the day when the incident was initiated. |
| country country_txt |
Country | categorical | This field identifies the country or location where the incident occurred. Separatist regions, such as Kashmir, Chechnya, South Ossetia, Transnistria, or Republic of Cabinda, are coded as part of the “home” country. |
| region region_txt |
Region | categorical | This field identifies the region in which the incident occurred. The regions are divided into the 13 categories, and dependent on the country coded for the case: North America, Central America & Caribbean, South America, East Asia, Southeast Asia, South Asia, Central Asia, Western Europe, Eastern Europe, Middle East & North Africa, Sub-Saharan Africa, amd Australasia & Oceania. |
| provstate | Province/State | text | This variable records the name (at the time of event) of the 1st order subnational administrative region in which the event occurs. |
| city | City | text | This field contains the name of the city, village, or town in which the incident occurred. If the city, village, or town for an incident is unknown, then this field contains the smallest administrative area below provstate which can be found for the incident (e.g., district). |
| attacktype1 attacktype1_txt |
Attack Type | categorical | This field captures the general method of attack and often reflects the broad class of tactics used. It consists of nine categories, which are listed here: Assassination, Hijacking, Kidnapping, Barricade Incident, Bombing/Explosion, Armed Assault, Unarmed Assault, Facility/Infrastructure Attack, Unknown. |
| targtype1 targtype1_txt |
Target Type | categorical | The target/victim type field captures the general type of target/victim. When a victim is attacked specifically because of his or her relationship to a particular person, such as a prominent figure, the target type reflects that motive. For example, if a family member of a government official is attacked because of his or her relationship to that individual, the type of target is “government.” This variable consists of 22 categories that can be found in the GTD codebook. |
| gname | Perpetrator Group Name | unstructured text | This field contains the name of the group that carried out the attack. In order to ensure consistency in the usage of group names for the database, the GTD database uses a standardized list of group names that have been established by project staff to serve as a reference for all subsequent entries. In the event that the name of a formal perpetrator group or organization is not reported in source materials, this field may contain relevant information about the generic identity of the perpetrator(s) (e.g., “Protestant Extremists”). Note that these categories do not represent discrete entities. They are not exhaustive or mutually exclusive (e.g., “student radicals” and “left-wing militants” may describe the same people). They also do not characterize the behavior of an entire population or ideological movement. For many attacks, generic identifiers are the only information available about the perpetrators. Because of this they are included in the database to provide context; however, analysis of generic identifiers should be interpreted with caution. |
| city | City | text | This field contains the name of the city, village, or town in which the incident occurred. If the city, village, or town for an incident is unknown, then this field contains the smallest administrative area below provstate which can be found for the incident (e.g., district). |
| motive | Motive | unstructured text | When reports explicitly mention a specific motive for the attack, this motive is recorded in the “Motive” field. This field may also include general information about the political, social, or economic climate at the time of the attack if considered relevant to the motivation underlying the incident. Note: This field is presently only systematically available with incidents occurring after 1997. |
| weaptype1 weaptype1_txt |
Weapon Type | categorical | This field records the general type of weapon used in the incident. It consists of the following categories: Biological, Chemical, Radiological, Nuclear, Firearms, Explosives, Fake Weapons, Incendiary, Melee, Vehicle, Sabotage Equipment, Other, and Unknown. |
| nkill | Total Number of Fatalities | ratio | This field stores the number of total confirmed fatalities for the incident. The number includes all victims and attackers who died as a direct result of the incident. Where there is evidence of fatalities, but a figure is not reported or it is too vague to be of use, such as “many” or “some,” this field remains blank. |
| nwound | Total Number of Injured | ratio | This field records the number of confirmed non-fatal injuries to both perpetrators and victims. It follows the conventions of the “Total Number of Fatalities” field described above. |
With the exception of gname and motivation, all the variables we have included in the data frame are structured and well-defined. For additional information on each of the variables and examples of how they are coded, see the GTD codebook. Now that we have our dataframe, we can proceed to some exploratory data analysis.
Let's begin by examining the correlation between the variables in our dataset using a heatmap. Note that we need to unpivot our categorical variables before attempting to identify correlation between variables in our dataset, otherwise the corr method will assume that the categorical numerical variables are interval variables. I.e. you could perform well-defined operations including North America+Southeast Asia=Central Asia. For more information on types of data and the operations you can perform on them see: Type of data but not data-types.
#import seaborn and matplotlib for visualization
import seaborn as sns
import matplotlib.pyplot as plt
#Make a copy of the data frame
pgtd = gtd.copy()
#Unpivot our categorical variables
pgtd = pd.concat([pgtd, pd.get_dummies(pgtd.region_txt, drop_first = False, prefix = 'region')], axis=1)
pgtd = pd.concat([pgtd, pd.get_dummies(pgtd.attacktype1_txt, drop_first = False, prefix = 'attack_type')], axis=1)
pgtd = pd.concat([pgtd, pd.get_dummies(pgtd.targtype1_txt, drop_first = False, prefix = 'target_type')], axis=1)
pgtd = pd.concat([pgtd, pd.get_dummies(pgtd.weaptype1_txt, drop_first = False, prefix = 'weapon_type')], axis=1)
#Drop the categorical variables from the dataframe
pgtd = pgtd.drop(columns = ['country', 'region', 'region_txt', 'attacktype1_txt', 'attacktype1',
'targtype1_txt', 'targtype1','weaptype1_txt','weaptype1'])
#use corr method to generate correlation matrix of correlation coefficients between each variable
corr = pgtd.corr()
#set figure size and shape
fig = plt.figure(figsize = (20,20), dpi = 50)
#create heatmap
ax = sns.heatmap(
corr,
vmin=-1, vmax=1, center=0, #correlation values between -1 and 1
cmap=sns.diverging_palette(20, 220, n=200), #diverging color pallette
square=True, #Make the matrix a square
linewidths=0.5
)
#One label has a particularly long name, so we manually shorten it
col_labels = list(corr.columns).copy()
col_labels[-1] = 'weapon_type_Vehicle'
#Set xticklabels to include new label
ax.set_xticklabels(
col_labels,
rotation=45,
horizontalalignment='right'
)
#Set xticklabels to include new label
ax.set_yticklabels(
col_labels
);
The plot above is a correlation matrix that shows the correlation coefficient between each ratio variable in the dataset. Unfortunately it does not seem as though there are many variables that are highly correlated in our dataset. While our visualization is fun, it is clunky and hard to interpret. Let's examine further by finding all of the pairs of variables who correlation coefficient is greater than 0.3 or less than -0.3. It is generally accepted, although arbitrary, that 0.3 represents a weak correlation between variables, between 0.3 and 0.7 implies moderate correlation and 0.7 or greater implies strong correlation between variables. For more information on correlation coefficients, see: Boston University Correlation and Regression.
k = 0 #k will count the number of pairs, so we can reference by number in our analysis
#Due to the symmetry of the matrix, we only need to iterate over the bottom left triangle.
for i in range(len(corr.columns)):
for j in range(i + 1, len(corr.columns)):
#If the correlation coefficient is greater than 0.3 print the pair
if abs(corr.iloc[i,j]) > 0.3:
k += 1
print(str(k)+".", corr.columns[i], corr.columns[j], corr.iloc[i,j])
1. iyear region_Central America & Caribbean -0.33253279811635467 2. iyear region_South America -0.3164820104707977 3. iyear region_Western Europe -0.3240107665572563 4. nkill nwound 0.5374432605321358 5. region_Middle East & North Africa region_South Asia -0.3652109879128187 6. attack_type_Armed Assault attack_type_Bombing/Explosion -0.5273801348933286 7. attack_type_Armed Assault weapon_type_Explosives -0.4996559996982958 8. attack_type_Armed Assault weapon_type_Firearms 0.6388349131961226 9. attack_type_Assassination attack_type_Bombing/Explosion -0.3233224606071381 10. attack_type_Bombing/Explosion weapon_type_Explosives 0.9228123910805127 11. attack_type_Bombing/Explosion weapon_type_Firearms -0.6394077534737226 12. attack_type_Facility/Infrastructure Attack weapon_type_Incendiary 0.7588477245915957 13. attack_type_Hostage Taking (Kidnapping) weapon_type_Unknown 0.3111172330604915 14. attack_type_Unarmed Assault weapon_type_Chemical 0.33355681982406926 15. attack_type_Unknown weapon_type_Unknown 0.7031133667414636 16. weapon_type_Explosives weapon_type_Firearms -0.6884922330769069 17. weapon_type_Explosives weapon_type_Unknown -0.31705097229117324
We have 17 pairs of variables that are at least weakly correlated. Let's see if any of the relationships are not obvious or artificial. The first threes pairs show a negative correlation between year and three region in the data sets. This likely points to a decrease in terrorism in Central America & Caribbean, South America, and Western Europe from 1970 through 2019. The fourth pair shows correlation between casualties and injuries, which is intuitive; the more people who are injured in an attack, the more likely there are to be casualties and vice-versa. Pairs six through eight indicate that there during incidents classifies as armed attacks there are typically firearms used rather than other types of weapons, this is obvious. Similarly the correlations in pairs 10 and 11, shows that during attack types where there is a bombing or explosive, there is a very strong correlation to the weapon type being a bomb; once again, this is obvious. Pair 12 shows that there is a strong correlation between incidents classified as attacks on infrastructure and the use of incindiery weapons. This likely just means that the number of arson cases in the GTD is far greater than any attacks on people using incindiery weapons. Pair 13 indicates that there is a weak correlation between incidents classified as kidnappings and incidents where the weapon type was unknown. Perplexingly, there is an interesting correlation between incidents classified assaults and the use of chemical weapons. This perhaps has to do with how incidents in the GTD are coded, but warrant further investigation. Pair 15 shows that there is a strong correlation between incidents where the weapon was unknown and incidents where the attack classification was unknown. This is likely a reflection of gaps in open-source data. Finally pairs 5, 16 and 17 are artificial correlations, since they were derived from the same variable and are therefore meaningless. Now that we have examined our correlated pairs for, let's investigate some of the less obvious correlations. Namely, the correlation between year and region; the correlation between incidiery weapons and attacks on infrastructure; and the correlation between unarmed assaults and the use of chemical weapons.
Let's continue by further exploring the relationship between year of a terrorist incident and the region in which the incident occurred. First let's plot some general information about terrorist attacks over time, such as the number of attacks per year and the number of casualties per year.
from collections import OrderedDict
#N_dict will store year:number of attack pairs, K_dict will store year:number of casualties pairs
N_dict = {}; K_dict = {}
#Fill the two dictionaries with year-number of attacks pairs, and year-number of casualties paris
for year in gtd.iyear.unique():
N_dict[year] = len(gtd[gtd['iyear'] == year])
K_dict[year] = gtd[gtd['iyear'] == year].nkill.sum()
#Order the dicts by year
N_dict = OrderedDict(sorted(N_dict.items()))
K_dict = OrderedDict(sorted(K_dict.items()))
#Set shape of plot
fig = plt.figure(figsize = (10,5), dpi = 150)
#Add the plot, plot title, and axes labels
ax = fig.add_subplot(title = 'Terrorist Attacks (1970-2019)', xlabel = 'Year')
#Set x-axis ticks to one per year from 1970-2019
plt.xticks(np.arange(1970, 2021, step=5), rotation = 45)
plt.xlim(1969, 2021)
#Plot Number of Terrorist Attacks per Year
ax.plot(N_dict.keys(),
N_dict.values(),
label = "Number of Terrorist Attacks",
alpha = 0.75)
#Plot Number of Casualties per Year
ax.plot(K_dict.keys(),
K_dict.values(),
label = "Number of Casualties from Terrorist Attacks",
alpha = 0.75)
#plot legend
plt.legend()
#add gridlines to plot
plt.grid(linewidth = 0.1)
The plot above shows both the number of terrorist attacks per year and the number of casualties from terrorist per year from 1970 through 2019. Immediately we see that there is not a linear relationship between attacks and time, or casualties and time. Generally, we see trends of increasing and decreasing attacks and casualties over time, with a notable spike followed by a rapid decline in 2014. The spike coincides with the formation of the Islamic State of Iraq and Syria (ISIS) in 2013, and its declaration of a caliphate in 2014.
There is a strong and intuitive relationship between number of attacks and number of casualties. Let's plot casualties per year as a function of attacks per year and see if there is a linear relationship. We hypothesize, if there is a significant linear relationship between attacks per year and casualties per year, then the slope of a best fit line will not equal zero. i.e.
$$H_0: B_1 = 0$$
$$H_1: B_1 \neq 0$$
where $B_1$ is the regression coefficient (or slope). We will choose a signifcance level of 0.05 for our test. We concede that the choice of 0.05 is relatively arbitrary; however it is the generally accepted value used when testing hypotheses. A significance level of 0.05 implies that there is a 5% chance we incorrectly reject the null hypothesis. We can then use a linear t-test to determine whether the slope of the regression line differs significantly from zero (i.e. whether we accept or reject the null hypothesis). We can use the statsmodel library to test our hypothesis:
from sklearn.linear_model import LinearRegression
KN_dict = {}; #KN_dict will store number of attack pairs per year: number of casualties per year pairs,
for year in gtd.iyear.unique():
KN_dict[len(gtd[gtd['iyear'] == year])] = gtd[gtd['iyear'] == year].nkill.sum()
KN_dict = OrderedDict(sorted(KN_dict.items()))
#We need to reshape our data to fit the data structure expected by the linear regression
X = np.array(list(KN_dict.keys())).reshape(-1, 1)
y = np.array(list(KN_dict.values())).reshape(-1, 1)
#fit linear regression to data
lr = LinearRegression().fit(X, y)
#print the equation of the model
print(
"Equation of Linear Model:\n"+
"Y = ", lr.coef_[0][0],"* X ", lr.intercept_[0])
Equation of Linear Model: Y = 2.4155987387383884 * X -606.7020623592907
#Set shape of plot
fig = plt.figure(figsize = (10,5), dpi = 150)
#Add the plot, plot title, and axes labels
ax = fig.add_subplot(title = 'Attacks per Year vs Casualties per Year (1970-2019)', xlabel = 'Number of Attacks per Year', ylabel = 'Casualties per Year')
#Plot Number of Terrorist Attacks per Year
ax.scatter(KN_dict.keys(),
KN_dict.values(),
label = "Number of Terrorist Attacks",
alpha = 0.5)
#Plot the linear regression of the Global Life Expectancy
ax.plot(X, lr.predict(X), label = "Linear Regression of Casualties per Attack")
#plot legend
plt.legend()
#add gridlines to plot
plt.grid(linewidth = 0.1)
In the plot above, we see the number of casualties from terrorist attacks per year plotted as a function of terrorist attacks per year. Visually, there is a strong positive, linear correlation, which is intuititive. The more attacks there are per year, the more casualties there are from attacks in a given year. Let's now test our hypothesis stated above.
import statsmodels.api as sm
X2 = sm.add_constant(X)
est = sm.OLS(y, X2)
est2 = est.fit()
print(est2.summary())
OLS Regression Results
==============================================================================
Dep. Variable: y R-squared: 0.940
Model: OLS Adj. R-squared: 0.939
Method: Least Squares F-statistic: 735.6
Date: Sat, 15 May 2021 Prob (F-statistic): 2.36e-30
Time: 11:20:09 Log-Likelihood: -450.76
No. Observations: 49 AIC: 905.5
Df Residuals: 47 BIC: 909.3
Df Model: 1
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------
const -606.7021 505.542 -1.200 0.236 -1623.722 410.318
x1 2.4156 0.089 27.121 0.000 2.236 2.595
==============================================================================
Omnibus: 0.168 Durbin-Watson: 1.552
Prob(Omnibus): 0.919 Jarque-Bera (JB): 0.044
Skew: 0.070 Prob(JB): 0.978
Kurtosis: 2.952 Cond. No. 8.22e+03
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 8.22e+03. This might indicate that there are
strong multicollinearity or other numerical problems.
Remember the p-value measures the probability of getting results at least as extreme as the ones you observed, given that the null hypothesis is true. (Source: Not even Scientists Can Easily Explain p-Values | FiveThirtyEight) The p-value is less than 0.05, in fact the p-value is less than 0.001, which implies there is less than a 0.1% chance that if the Null Hypothesis was true, then we would see data at least as correlated as the gapminder data actually is. Therefore, we reject the null hypothesis. Furthermore, let's look at the Pearson correlation coefficient between the variables.
import scipy.stats
scipy.stats.pearsonr(list(KN_dict.keys()), list(KN_dict.values()))[0]
0.9695054887279733
A correlation coefficient greater than 0.7 implies that the number of attacks per year and the number of casualties per year are strongly correlated. Let's investigate if this trend holds if we disambiguate by region. First let's replicate the plots of attacks per year and casualties per year disambiguated by region.
from matplotlib import cm
#unique list of regions from the dataframe
regions = gtd.region_txt.unique()
#diverging colors for each plot
colors = [ cm.gist_ncar(int(x*256/len(regions))) for x in range(len(regions))]
#Set shape of plot: 12 subplots (one for each region)
fig, axs = plt.subplots(len(regions), figsize = (10, 60), dpi = 150)
for i in range(len(regions)):
N_dict = {}; K_dict = {} #N_dict will store year:number of attack pairs, K_dict will store year:number of casualties pairs
for year in gtd.iyear.unique():
N_dict[year] = len(gtd[(gtd['iyear'] == year) & (gtd['region_txt'] == regions[i])])
K_dict[year] = gtd[(gtd['iyear'] == year) & (gtd['region_txt'] == regions[i])].nkill.sum()
#order the dictionaries by Year
N_dict = OrderedDict(sorted(N_dict.items()))
K_dict = OrderedDict(sorted(K_dict.items()))
#Set x-axis ticks to one per year from 1970-2020
plt.xticks(np.arange(1970, 2021, step=5), rotation = 45)
plt.xlim(1969, 2021)
#Plot Number of Terrorist Attacks per Year
atk = axs[i].plot(N_dict.keys(),
N_dict.values(),
label = "Attacks",
color = colors[i])
#alpha = 1)
#We use twinx to create a secondary y-axis
ax2 = axs[i].twinx()
#Plot Number of Casualties per Year
cas = ax2.plot(K_dict.keys(),
K_dict.values(),
label = "Casualties",
color = colors[i],
linestyle = "dashed",
alpha = 0.75)
#set second Y-axis label
ax2.set_ylabel('Casualties')
#add gridlines to plot
axs[i].grid(linewidth = 0.1)
#set title and axis labels
axs[i].set_title('Terrorist Attacks and Casualties by Year ('+regions[i]+')')
plt.setp(axs[i], xlabel = 'Year')
plt.setp(axs[i], ylabel = 'Number of Attacks')
plt.setp(axs[i], xticks = np.arange(1970, 2021, step=5))
#Legend: Because we use two y-axes we actually need to "combine" the plots to get a single legend
lns = atk+cas # "add" two lines together
labs = [l.get_label() for l in lns] #get labels for all the labels in both lines
axs[i].legend(lns, labs, loc=0) #plot legend to include both lines and labels
fig.tight_layout()
plt.show()
The plots above show terrorist attacks per year and casualties per year disambiguated by region. Immediately we see strong correlations between casualties per year and attacks per year with a few exceptions, specifically North America, Southeast Asia, Western Europe, and East Asia. Again, we will plot Casualties per year as a function of attacks per year, now for each region, and we test the following hypothesis for each region: We hypothesize, if there is a significant linear relationship between attacks per year and casualties per year (in each region), then the slope of a best fit line will not equal zero. i.e. $$H_0: B_1 = 0$$ $$H_1: B_1 \neq 0$$ where $B_1$ is the regression coefficient (or slope). We will choose a signifcance level of 0.05 for our test.
#Set shape of plot: 12 subplots
fig, axs = plt.subplots(len(regions), figsize = (10, 60), dpi = 150)
for i in range(len(regions)):
KN_dict = {}; #N_dict will store year:number of attack pairs, K_dict will store year:number of casualties pairs
for year in gtd.iyear.unique():
KN_dict[len(gtd[(gtd['iyear'] == year) & (gtd['region_txt'] == regions[i])])] = gtd[(gtd['iyear'] == year) & (gtd['region_txt'] == regions[i])].nkill.sum()
KN_dict = OrderedDict(sorted(KN_dict.items()))
#We need to reshape our data to fit the data structure expected by the linear regression
X = np.array(list(KN_dict.keys())).reshape(-1, 1)
y = np.array(list(KN_dict.values())).reshape(-1, 1)
#fit linear regression to data
lr = LinearRegression().fit(X, y)
X2 = sm.add_constant(X)
est = sm.OLS(y, X2)
est2 = est.fit()
#Print results of statistical test
print(regions[i])
print(est2.summary())
#Plot Number of Terrorist Attacks per Year
axs[i].scatter(KN_dict.keys(),
KN_dict.values(),
label = "Casulties per year vs Attacks per year",
color = colors[i],
alpha = 0.5)
#Plot the linear regression of the Global Life Expectancy
axs[i].plot(X, lr.predict(X),
label = "Linear Regression of Casualties per Attack",
color = colors[i])
#add gridlines to plot
axs[i].grid(linewidth = 0.1)
#set title and axis labels
axs[i].set_title('Terrorist Casualties per year vs. Attacks per year ('+regions[i]+')')
plt.setp(axs[i], xlabel = 'Number of Attacks per year')
plt.setp(axs[i], ylabel = 'Number of Casualties per year')
#Legend
axs[i].legend()
fig.tight_layout()
plt.show()
Central America & Caribbean
OLS Regression Results
==============================================================================
Dep. Variable: y R-squared: 0.661
Model: OLS Adj. R-squared: 0.650
Method: Least Squares F-statistic: 60.36
Date: Sat, 15 May 2021 Prob (F-statistic): 9.13e-09
Time: 11:32:05 Log-Likelihood: -270.24
No. Observations: 33 AIC: 544.5
Df Residuals: 31 BIC: 547.5
Df Model: 1
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------
const -194.8476 207.771 -0.938 0.356 -618.599 228.903
x1 3.4056 0.438 7.769 0.000 2.512 4.300
==============================================================================
Omnibus: 26.782 Durbin-Watson: 2.322
Prob(Omnibus): 0.000 Jarque-Bera (JB): 55.424
Skew: 1.862 Prob(JB): 9.22e-13
Kurtosis: 8.142 Cond. No. 629.
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
North America
OLS Regression Results
==============================================================================
Dep. Variable: y R-squared: 0.001
Model: OLS Adj. R-squared: -0.024
Method: Least Squares F-statistic: 0.02253
Date: Sat, 15 May 2021 Prob (F-statistic): 0.881
Time: 11:32:06 Log-Likelihood: -324.01
No. Observations: 43 AIC: 652.0
Df Residuals: 41 BIC: 655.5
Df Model: 1
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------
const 120.2916 101.385 1.186 0.242 -84.460 325.043
x1 -0.1387 0.924 -0.150 0.881 -2.005 1.728
==============================================================================
Omnibus: 96.686 Durbin-Watson: 2.083
Prob(Omnibus): 0.000 Jarque-Bera (JB): 2696.840
Skew: 6.179 Prob(JB): 0.00
Kurtosis: 39.777 Cond. No. 157.
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
Southeast Asia
OLS Regression Results
==============================================================================
Dep. Variable: y R-squared: 0.609
Model: OLS Adj. R-squared: 0.601
Method: Least Squares F-statistic: 73.18
Date: Sat, 15 May 2021 Prob (F-statistic): 3.84e-11
Time: 11:32:07 Log-Likelihood: -310.64
No. Observations: 49 AIC: 625.3
Df Residuals: 47 BIC: 629.1
Df Model: 1
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------
const 195.2488 26.464 7.378 0.000 142.010 248.488
x1 0.5161 0.060 8.555 0.000 0.395 0.638
==============================================================================
Omnibus: 5.504 Durbin-Watson: 1.057
Prob(Omnibus): 0.064 Jarque-Bera (JB): 4.929
Skew: 0.776 Prob(JB): 0.0851
Kurtosis: 3.087 Cond. No. 581.
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
Western Europe
OLS Regression Results
==============================================================================
Dep. Variable: y R-squared: 0.330
Model: OLS Adj. R-squared: 0.316
Method: Least Squares F-statistic: 23.16
Date: Sat, 15 May 2021 Prob (F-statistic): 1.58e-05
Time: 11:32:08 Log-Likelihood: -294.93
No. Observations: 49 AIC: 593.9
Df Residuals: 47 BIC: 597.6
Df Model: 1
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------
const 23.6600 27.506 0.860 0.394 -31.676 78.996
x1 0.3235 0.067 4.812 0.000 0.188 0.459
==============================================================================
Omnibus: 14.415 Durbin-Watson: 2.085
Prob(Omnibus): 0.001 Jarque-Bera (JB): 15.678
Skew: 1.302 Prob(JB): 0.000394
Kurtosis: 3.946 Cond. No. 776.
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
East Asia
OLS Regression Results
==============================================================================
Dep. Variable: y R-squared: 0.031
Model: OLS Adj. R-squared: -0.005
Method: Least Squares F-statistic: 0.8525
Date: Sat, 15 May 2021 Prob (F-statistic): 0.364
Time: 11:32:10 Log-Likelihood: -159.63
No. Observations: 29 AIC: 323.3
Df Residuals: 27 BIC: 326.0
Df Model: 1
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------
const 20.6693 16.217 1.275 0.213 -12.605 53.944
x1 0.4284 0.464 0.923 0.364 -0.524 1.380
==============================================================================
Omnibus: 54.188 Durbin-Watson: 2.061
Prob(Omnibus): 0.000 Jarque-Bera (JB): 336.576
Skew: 3.770 Prob(JB): 8.19e-74
Kurtosis: 17.889 Cond. No. 49.5
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
South America
OLS Regression Results
==============================================================================
Dep. Variable: y R-squared: 0.896
Model: OLS Adj. R-squared: 0.893
Method: Least Squares F-statistic: 402.8
Date: Sat, 15 May 2021 Prob (F-statistic): 1.08e-24
Time: 11:32:11 Log-Likelihood: -339.81
No. Observations: 49 AIC: 683.6
Df Residuals: 47 BIC: 687.4
Df Model: 1
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------
const -73.2612 49.259 -1.487 0.144 -172.357 25.835
x1 1.6751 0.083 20.070 0.000 1.507 1.843
==============================================================================
Omnibus: 5.307 Durbin-Watson: 1.504
Prob(Omnibus): 0.070 Jarque-Bera (JB): 5.037
Skew: 0.397 Prob(JB): 0.0806
Kurtosis: 4.355 Cond. No. 802.
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
Eastern Europe
OLS Regression Results
==============================================================================
Dep. Variable: y R-squared: 0.778
Model: OLS Adj. R-squared: 0.771
Method: Least Squares F-statistic: 119.0
Date: Sat, 15 May 2021 Prob (F-statistic): 1.20e-12
Time: 11:32:12 Log-Likelihood: -226.38
No. Observations: 36 AIC: 456.8
Df Residuals: 34 BIC: 459.9
Df Model: 1
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------
const 4.9952 28.160 0.177 0.860 -52.233 62.224
x1 1.3130 0.120 10.908 0.000 1.068 1.558
==============================================================================
Omnibus: 29.052 Durbin-Watson: 2.177
Prob(Omnibus): 0.000 Jarque-Bera (JB): 63.383
Skew: 1.957 Prob(JB): 1.72e-14
Kurtosis: 8.190 Cond. No. 295.
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
Sub-Saharan Africa
OLS Regression Results
==============================================================================
Dep. Variable: y R-squared: 0.864
Model: OLS Adj. R-squared: 0.861
Method: Least Squares F-statistic: 286.9
Date: Sat, 15 May 2021 Prob (F-statistic): 3.78e-21
Time: 11:32:14 Log-Likelihood: -392.86
No. Observations: 47 AIC: 789.7
Df Residuals: 45 BIC: 793.4
Df Model: 1
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------
const 136.1625 187.421 0.727 0.471 -241.323 513.648
x1 3.9266 0.232 16.938 0.000 3.460 4.393
==============================================================================
Omnibus: 25.811 Durbin-Watson: 1.970
Prob(Omnibus): 0.000 Jarque-Bera (JB): 60.159
Skew: 1.445 Prob(JB): 8.64e-14
Kurtosis: 7.729 Cond. No. 984.
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
Middle East & North Africa
OLS Regression Results
==============================================================================
Dep. Variable: y R-squared: 0.940
Model: OLS Adj. R-squared: 0.939
Method: Least Squares F-statistic: 721.6
Date: Sat, 15 May 2021 Prob (F-statistic): 9.26e-30
Time: 11:32:15 Log-Likelihood: -407.92
No. Observations: 48 AIC: 819.8
Df Residuals: 46 BIC: 823.6
Df Model: 1
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------
const -177.7555 212.549 -0.836 0.407 -605.594 250.083
x1 2.7580 0.103 26.862 0.000 2.551 2.965
==============================================================================
Omnibus: 7.924 Durbin-Watson: 2.007
Prob(Omnibus): 0.019 Jarque-Bera (JB): 8.197
Skew: 0.619 Prob(JB): 0.0166
Kurtosis: 4.603 Cond. No. 2.51e+03
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 2.51e+03. This might indicate that there are
strong multicollinearity or other numerical problems.
Australasia & Oceania
OLS Regression Results
==============================================================================
Dep. Variable: y R-squared: 0.050
Model: OLS Adj. R-squared: -0.003
Method: Least Squares F-statistic: 0.9436
Date: Sat, 15 May 2021 Prob (F-statistic): 0.344
Time: 11:32:16 Log-Likelihood: -77.748
No. Observations: 20 AIC: 159.5
Df Residuals: 18 BIC: 161.5
Df Model: 1
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------
const 3.6688 4.827 0.760 0.457 -6.472 13.809
x1 0.3666 0.377 0.971 0.344 -0.426 1.160
==============================================================================
Omnibus: 35.325 Durbin-Watson: 2.378
Prob(Omnibus): 0.000 Jarque-Bera (JB): 83.552
Skew: 2.886 Prob(JB): 7.19e-19
Kurtosis: 11.183 Cond. No. 22.3
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
South Asia
OLS Regression Results
==============================================================================
Dep. Variable: y R-squared: 0.870
Model: OLS Adj. R-squared: 0.867
Method: Least Squares F-statistic: 281.3
Date: Sat, 15 May 2021 Prob (F-statistic): 3.19e-20
Time: 11:32:17 Log-Likelihood: -367.78
No. Observations: 44 AIC: 739.6
Df Residuals: 42 BIC: 743.1
Df Model: 1
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------
const 582.2199 205.418 2.834 0.007 167.670 996.769
x1 1.8657 0.111 16.772 0.000 1.641 2.090
==============================================================================
Omnibus: 31.915 Durbin-Watson: 0.956
Prob(Omnibus): 0.000 Jarque-Bera (JB): 85.154
Skew: 1.850 Prob(JB): 3.23e-19
Kurtosis: 8.723 Cond. No. 2.38e+03
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 2.38e+03. This might indicate that there are
strong multicollinearity or other numerical problems.
Central Asia
OLS Regression Results
==============================================================================
Dep. Variable: y R-squared: 0.670
Model: OLS Adj. R-squared: 0.653
Method: Least Squares F-statistic: 40.58
Date: Sat, 15 May 2021 Prob (F-statistic): 3.24e-06
Time: 11:32:18 Log-Likelihood: -111.85
No. Observations: 22 AIC: 227.7
Df Residuals: 20 BIC: 229.9
Df Model: 1
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------
const -18.3928 13.257 -1.387 0.181 -46.045 9.260
x1 2.6826 0.421 6.370 0.000 1.804 3.561
==============================================================================
Omnibus: 17.561 Durbin-Watson: 1.282
Prob(Omnibus): 0.000 Jarque-Bera (JB): 21.230
Skew: 1.568 Prob(JB): 2.45e-05
Kurtosis: 6.650 Cond. No. 47.8
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
The above plots show the number of casualties per year vs the number of attacks per year with one plot for each region in the table. Additionally, provide the results of our hypothesis tests below.
| Region | p-value | Statistical Significant? | Casualties/Attack (Slope) |
|---|---|---|---|
| Central America & Caribbean | 0.000 | Yes | 3.41 |
| North America | 0.242 | No | -0.14* |
| South East Asia | 0.000 | Yes | 0.52 |
| Western Europe | 0.000 | Yes | 0.32 |
| East Asia | 0.364 | No | 0.43* |
| South America | 0.000 | Yes | 1.68 |
| Eastern Europe | 0.000 | Yes | 1.31 |
| Sub-Saharan Africa | 0.000 | Yes | 3.93 |
| Middle East & North Africa | 0.000 | Yes | 2.76 |
| Australasia & Oceania | 0.344 | No | 0.36* |
| South Asia | 0.000 | Yes | 0.87 |
| Central Asia | 0.000 | Yes | 2.68 |
A * denotes that the casualties per attack, or slope, for a region is not a meaningful measure because there is no statistically significant linear relationship between the number of casualties per year and the number of attacks per year.
We see that all the regions in the dataset have statistically significant relationships between number of attacks per year and number of casualties per year except North America, East Asia, and Australasia & Oceania. Each of the three regions has one year with one significant outlier where casualties far outpaced the expected casualties given the number of attacks in a year. In North America, the outlier year corresponds to 2001, the year in which the September 11th Attacks took place. In East Asia, the outlier corresponds to attacks in China from Uighur Separatists throughout 2014. In Australasia & Oceania, the outlier corresponds to the 2019 Christchurch mosque shootings.
Through our hypothesis tests, we have shown that globally there is a statistically significant linear relationship between the number of terrorist attacks in a given year and the the number of casualties from terrorist attacks in a given year. We show this relationship is still statistically significant when you disambiguate by region with the exception of North America, East Asia, and Australasia & Oceania. In all three regions there was one significant outlier, where the number of casualties in a given year far exceeded the expected number of casualties based on our linear model. On the surface level, there does not seem to be any obvious similarities between the three outliers that would help create policy to mitigate the harm done by a single attack. The outlier years in North America and Australasia & Oceania both corresponded to a single incident in their given years, while the outlier year in East Asia corresponded to rising violence throughout an entire year. The motivation for the incidents for all three regions were different as well. In the case of the September 11 Attacks, Osama bin Laden stated in his 2002 "Letter to America" that al-Qaeda's attacks were motivated by U.S. occupation in the Middle East as well as U.S. support of governments who were in active conflicts against Muslims around the world, such as Israel, Somalia, Philippines, Russia, and India [Source: The Guardian]. In the case of the Christchurch mosque shootings, the perpetrator, Brenton Tarrant, was motivated by white supremacy, xenophobia and islamophobia [Source: Time]. In the case of the Xinjiang conflict, Uighur Separatists, backed by affiliates of the Islamic State and al-Qaeda, sought to separate from China in order to escape religious persecution. For more information about the conflict, see Devastating Blows: Religious Repression of Uighurs in Xinjiang, The Xinjiang Papers, and Xinjiang Attacks. None of the attacks across regions used the same types of weapons or had similar target types.
Overall there does not seem like there is much insight we gained from this analysis. We are no closer to understanding why these outliers exist, or how to prevent them.
After the disappointing first act, we will attempty to extrapolate information about motives of perpetrators in the Global Terrorism Database. The GTD does not have a categorical variable for motivation, but instead has an unstructured variable that we will attempt to analyze. Let's begin by cleaning the motivation variable and performing some exploratory data analysis. To clean the variable, we will drop all rows where motivation is missing. Additionally, we will alter the rows so they contain no punctuation and are all in lowercase.
import re #We will use regular expressions to manipulate and parse the unstructured text in the motive column
print("Total number of incidents:" len(gtd))
gtd = gtd.dropna(subset = ["motive"])
print("Number of incidents with non-NAN motives:" len(gtd))
#remove any punctuation
gtd['motive'] = gtd['motive'].map(lambda x: re.sub('[,\.!?]', '', x))
#make all letters lowercase
gtd['motive'] = gtd['motive'].map(lambda x: x.lower())
#print the first five rows
gtd['motive'].head()
201183 53629
5 to protest the cairo illinois police deparment 8 to protest the war in vietnam and the draft 9 to protest the war in vietnam and the draft 11 protest the draft and vietnam war 14 to protest united states owned businesses in p... Name: motive, dtype: object
We start by dropping any incidents with no motive coded. We started with 201,183 incidents and find we have 53,629 with a motivation coded. Additionally we removed punctuation from the column and made each entry lowercase.
# Import the wordcloud library
from wordcloud import WordCloud
# Join the different processed motivations together.
long_string = ','.join(list(gtd['motive'].values))
# Create a WordCloud object
wordcloud = WordCloud(background_color="white", max_words=5000, contour_width=3, contour_color='steelblue', width = 800, height = 400)
# Generate a word cloud
wordcloud.generate(long_string)
# Visualize the word cloud
wordcloud.to_image()
Immediately, we see that there are a lot of frequent terms that aren't useful to us, such as "unknwon", "specific", and "motive". It seems as though we will need to process the data further and remove words from the corpus that are not insightful. We will use the gensim and nltk libraries to further process the data, by parsing each entry into a list words and removing stop words, terms that we deem irrelevant, from the corpus. Finally we revisualize, the parsed corpus, absent stop words, as a word cloud.
import gensim #import gensim
from gensim.utils import simple_preprocess
import nltk #import natural language tool-kit
nltk.download('stopwords') #download stopwords file locally
from nltk.corpus import stopwords #import stop word
#NLTK contains a default list of stop words incuding, "the", "a", "an", and "in" that are semantically meaningless
stop_words = stopwords.words('english')
#We extend the corpus with words that appear frequently, but are semantically meaningless to motivation
stop_words.extend(['unknown', 'attack', 'claimed', 'responsibility', 'incident', 'sources', 'stated', 'specific',
'however', 'noted', 'motive', 'carried','part', 'occurred', 'targeted', 'suspected', 'majority',
'recent', 'attacks', 'larger', 'trend', 'may', 'violence', 'related', 'al','also'])
#Given a list of sentences, parse each sentence into a list of words
def sent_to_words(sentences):
for sentence in sentences:
yield(gensim.utils.simple_preprocess(str(sentence), deacc=True))
#Given a document, remove all stopwords from the document
def remove_stopwords(texts):
return [[word for word in simple_preprocess(str(doc))
if word not in stop_words] for doc in texts]
#data is a list of documents in the form of each motivation entry
data = gtd.motive.values.tolist()
#convert the list of "documents" in a list of words
data_words = list(sent_to_words(data))
# remove stop words
data_words = remove_stopwords(data_words)
#print the first parsed "document"
print(data_words[:1][0])
# Join the different processed motivations together.
long_string = ','.join(str(item) for innerlist in data_words for item in innerlist)
# Create a WordCloud object
wordcloud = WordCloud(background_color="white", max_words=5000, contour_width=3, contour_color='steelblue', width = 800, height = 400)
# Generate a word cloud
wordcloud.generate(long_string)
# Visualize the word cloud
wordcloud.to_image()
/home/tclark/anaconda3/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above. and should_run_async(code) [nltk_data] Downloading package stopwords to /home/tclark/nltk_data... [nltk_data] Package stopwords is already up-to-date!
['protest', 'cairo', 'illinois', 'police', 'deparment']
The world cloud above is vastly different than the one we started with, but it is still not very informative. The largest terms often refer to groups or locations rather than an attacks ideological motivation. For example, "Islamic State" and "ISIL" both refer to a group name, while "Iraq" and "Levant" refer to places, but also possibly the group ISIS. We are starting to see more informative motivations such as "Sectarian Pakistan", "Destablize Algeria", and "Elections scheduled".
import gensim.corpora as corpora
# Create Dictionary
id2word = corpora.Dictionary(data_words)
# Create Corpus
texts = data_words
# Term Document Frequency - contains tuple where the first term is a number representing that unique word and the second is its term frequency
corpus = [id2word.doc2bow(text) for text in texts]
# View
print(corpus[:1][0][:30])
/home/tclark/anaconda3/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above. and should_run_async(code)
[(0, 1), (1, 1), (2, 1), (3, 1), (4, 1)]
[['protest', 'cairo', 'illinois', 'police', 'deparment'], ['protest', 'war', 'vietnam', 'draft'], ['protest', 'war', 'vietnam', 'draft'], ['protest', 'draft', 'vietnam', 'war'], ['protest', 'united', 'states', 'owned', 'businesses', 'puerto', 'rico'], ['motives', 'protest', 'vietnam', 'war', 'show', 'support', 'black', 'panther', 'party', 'show', 'support', 'young', 'lords'], ['retaliation', 'store', 'owner', 'shot', 'killed', 'african', 'american', 'attempting', 'commit', 'robbery', 'store'], ['took', 'place', 'disturbances', 'black', 'student', 'union', 'university'], ['took', 'place', 'heightened', 'antiwar', 'sentiments', 'seattle', 'washington'], ['intimidate', 'black', 'panther', 'party'], ['violent', 'iowa', 'beef', 'packers', 'strike', 'south', 'sioux', 'city'], ['prevent', 'african', 'american', 'community', 'west', 'point', 'mississippi', 'organizing'], ['kill', 'police'], ['african', 'american', 'opposition', 'school', 'integration', 'plan', 'instituted', 'federal', 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'raising', 'dinner', 'hilton', 'hotel', 'raise', 'money', 'election'], [], [], [], [], [], ['communist', 'order', 'consisted', 'revolutionary', 'leftists', 'attempting', 'expropriate', 'funds', 'establishment', 'order', 'improve', 'conditions', 'poor', 'armed', 'robberies', 'viewed', 'way', 'procure', 'weapons', 'safe', 'houses', 'would', 'used', 'armed', 'struggle', 'power'], [], ['united', 'freedom', 'front', 'believed', 'marxist', 'philosophy', 'opposed', 'us', 'militarism', 'central', 'america', 'group', 'sought', 'redistribute', 'economic', 'wealth', 'us', 'used', 'bank', 'robberies', 'finance', 'goals', 'additionally', 'efforts', 'improve', 'prison', 'life'], [], ['coincided', 'statehood', 'controversy', 'swearing', 'puerto', 'rico', 'new', 'governor', 'carlos', 'romero', 'barcelo', 'receiving', 'threats', 'bomb', 'campaign', 'would', 'coincide', 'new', 'administration'], ['protest', 'practice', 'abortion', 'sabotage', 'abortion', 'facilities', 'operating', 'minnesota'], 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'struggle', 'power'], [], ['group', 'advocated', 'independence', 'puerto', 'rico'], ['hours', 'demonstration', 'protesting', 'treatment', 'blacks', 'racially', 'divided', 'community', 'several', 'weeks', 'prior', 'unrest', 'community', 'whites', 'blacks', 'blacks', 'protesting', 'racial', 'economic', 'political', 'injustices', 'asking', 'better', 'jobs', 'representation', 'local', 'government'], ['perpetrators', 'say', 'intended', 'shots', 'call', 'attention', 'burning', 'crosses', 'blacks', 'away', 'time', 'racial', 'tensions'], ['communist', 'order', 'consisted', 'revolutionary', 'leftists', 'attempting', 'expropriate', 'funds', 'establishment', 'order', 'improve', 'conditions', 'poor', 'armed', 'robberies', 'viewed', 'way', 'procure', 'weapons', 'safe', 'houses', 'would', 'used', 'armed', 'struggle', 'power'], ['joseph', 'paul', 'franklin', 'former', 'member', 'ku', 'klux', 'klan', 'american', 'nazi', 'party', 'believed', 'upholding', 'honor', 'white', 'race', 'attacking', 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'order', 'order', 'ii', 'government', 'group', 'believed', 'unrighteous'], ['protest', 'presence', 'united', 'states', 'military', 'puerto', 'rico'], ['assailants', 'wrote', 'note', 'stating', 'tracy', 'spy', 'cia'], [], ['protest', 'plans', 'united', 'states', 'government', 'train', 'contras', 'nicaraguan', 'armed', 'rebels', 'puerto', 'rico'], ['protest', 'plans', 'united', 'states', 'government', 'train', 'contras', 'nicaraguan', 'armed', 'rebels', 'puerto', 'rico'], ['protest', 'plans', 'united', 'states', 'government', 'train', 'contras', 'nicaraguan', 'armed', 'rebels', 'puerto', 'rico'], ['protest', 'plans', 'united', 'states', 'government', 'train', 'contras', 'nicaraguan', 'armed', 'rebels', 'puerto', 'rico'], ['protest', 'plans', 'united', 'states', 'government', 'train', 'contras', 'nicaraguan', 'armed', 'rebels', 'puerto', 'rico'], ['protest', 'plans', 'united', 'states', 'government', 'train', 'contras', 'nicaraguan', 'armed', 'rebels', 'puerto', 'rico'], ['protest', 'plans', 'united', 'states', 'government', 'train', 'contras', 'nicaraguan', 'armed', 'rebels', 'puerto', 'rico'], ['protest', 'practice', 'abortion', 'sabotage', 'abortion', 'facilities', 'operating', 'new', 'york'], ['protest', 'practice', 'abortion', 'sabotage', 'abortion', 'facilities', 'operating', 'illinois'], ['protest', 'practice', 'abortion', 'sabotage', 'abortion', 'facilities', 'operating', 'new', 'york'], ['protest', 'practice', 'abortion', 'sabotage', 'abortion', 'facilities', 'operating', 'illinois'], ['protest', 'practice', 'abortion', 'sabotage', 'abortion', 'facilities', 'operating', 'michigan'], ['protest', 'practice', 'abortion', 'sabotage', 'abortion', 'facilities', 'operating', 'michigan'], ['protest', 'practice', 'abortion', 'sabotage', 'abortion', 'facilities', 'operating', 'illinois'], ['protest', 'practice', 'abortion', 'sabotage', 'abortion', 'facilities', 'operating', 'new', 'york'], [], [], ['protest', 'practice', 'abortion', 'sabotage', 'abortion', 'facilities', 'operating', 'illinois'], ['protest', 'practice', 'abortion', 'sabotage', 'abortion', 'facilities', 'operating', 'minnesota'], ['protest', 'practice', 'abortion', 'sabotage', 'abortion', 'facilities', 'operating', 'ohio'], ['protest', 'irs', 'taxation', 'system', 'united', 'states', 'government'], ['protest', 'practice', 'abortion', 'sabotage', 'abortion', 'facilities', 'operating', 'ohio'], ['protest', 'torture', 'thousands', 'animals', 'campus', 'labs', 'year'], ['white', 'supremacist', 'group', 'aryan', 'nation', 'tired', 'harassed', 'law', 'enforcement', 'agencies'], ['protest', 'united', 'states', 'based', 'businesses', 'send', 'supplies', 'cuba'], ['protest', 'united', 'states', 'based', 'businesses', 'send', 'supplies', 'cuba'], ['protest', 'united', 'states', 'presence', 'puerto', 'rico'], ['protest', 'practice', 'abortion', 'sabotage', 'abortion', 'facilities', 'operating', 'ohio'], ['protest', 'practice', 'abortion', 'sabotage', 'abortion', 'facilities', 'operating', 'california'], ['protest', 'united', 'states', 'based', 'institutions', 'send', 'supplies', 'cuba'], ['protest', 'practice', 'abortion', 'sabotage', 'abortion', 'facilities', 'operating', 'north', 'dakota'], ['protest', 'vietnamese', 'organizations', 'solicited', 'advertisements', 'companies', 'associated', 'vietnam', 'communist', 'government'], ['protest', 'united', 'states', 'based', 'businesses', 'send', 'supplies', 'cuba'], ['avenge', 'deaths', 'calves', 'taken', 'mothers', 'chained', 'brief', 'lives'], ['protest', 'practice', 'abortion', 'sabotage', 'abortion', 'facilities', 'operating', 'minnesota'], ['protest', 'practice', 'abortion', 'sabotage', 'abortion', 'facilities', 'operating', 'minnesota'], ['protest', 'practice', 'abortion', 'sabotage', 'abortion', 'facilities', 'operating', 'minnesota'], ['protest', 'practice', 'abortion', 'sabotage', 'abortion', 'facilities', 'operating', 'minnesota'], ['mountain', 'peaks', 'ski', 'resort', 'sat', 'considered', 'sacred', 'navajo', 'hopi', 'indian', 'tribes', 'perpetrators', 'criticized', 'us', 'forest', 'service', 'allowing', 'ski', 'area', 'built', 'back'], ['protest', 'destruction', 'trees', 'development', 'ski', 'resort', 'skiing', 'activity', 'land'], ['protest', 'hundreds', 'thousands', 'cattle', 'slaughtered', 'year', 'america', 'living', 'lives', 'misery', 'appease', 'flash', 'eating', 'tendencies', 'america'], ['protest', 'slaughter', 'treatment', 'animals'], [], ['protest', 'united', 'states', 'based', 'businesses', 'send', 'supplies', 'cuba'], ['prevent', 'mexican', 'government', 'extraditing', 'william', 'morales', 'member', 'faln', 'united', 'states'], ['commemorate', 'mormon', 'radical', 'john', 'singer', 'protest', 'lds', 'church', 'swapp', 'hoped', 'actions', 'would', 'lead', 'resurrection', 'john', 'singer'], ['protest', 'practice', 'abortion', 'sabotage', 'abortion', 'facilities', 'operating', 'washington'], ['protest', 'practice', 'abortion', 'sabotage', 'abortion', 'facilities', 'operating', 'colorado'], [], [], ['protest', 'american', 'presence', 'puerto', 'rico'], [], ['hijackers', 'demanded', 'arab', 'convicted', 'bombers', 'held', 'kuwaiti', 'prisons', 'released', 'exchange', 'safe', 'release', 'passengers'], ['liberation', 'tigers', 'tamil', 'eelam', 'ltte', 'fighting', 'separate', 'tamil', 'state'], ['protest', 'decision', 'cuban', 'museum', 'arts', 'culture', 'open', 'exhibit', 'featuring', 'works', 'cuban', 'artist'], ['call', 'release', 'orlando', 'bosch', 'anti', 'castro', 'militant', 'protest', 'policies', 'institute', 'cuban', 'studies'], ['protest', 'practice', 'abortion', 'sabotage', 'abortion', 'facilities', 'operating', 'california'], ['groups', 'setting', 'arson', 'various', 'meat', 'facilities', 'santa', 'clara', 'area', 'california', 'sun', 'valley', 'meat', 'company', 'responsible', 'packing', 'delivering', 'meat', 'restaurants'], ['protest', 'irs', 'taxation', 'system', 'united', 'states', 'government'], [], ['protest', 'united', 'states', 'based', 'businesses', 'send', 'supplies', 'cuba'], ['protest', 'policies', 'reunion', 'flotilla'], ['protest', 'irs', 'taxation', 'system', 'united', 'states', 'government'], ['group', 'responsible', 'multiple', 'incidents', 'sabotage', 'power', 'lines', 'ski', 'equipment'], ['protest', 'practice', 'abortion', 'sabotage', 'abortion', 'facilities', 'operating', 'california'], ['protest', 'destruction', 'trees', 'development', 'ski', 'resort', 'skiing', 'activity', 'land'], ['raise', 'awareness', 'culpability', 'american', 'companies', 'including', 'general', 'electric', 'causing', 'environmental', 'pollution', 'puerto', 'rico'], ['raise', 'awareness', 'culpability', 'american', 'companies', 'including', 'motorola', 'causing', 'environmental', 'pollution', 'puerto', 'rico'], ['protest', 'testing', 'surgical', 'stapling', 'products', 'live', 'dogs', 'frighten', 'company', 'chairman', 'halting', 'practice'], ['protest', 'practice', 'abortion', 'sabotage', 'abortion', 'facilities', 'operating', 'texas'], ['protest', 'practice', 'abortion', 'sabotage', 'abortion', 'facilities', 'operating', 'texas'], ['protest', 'practice', 'abortion', 'sabotage', 'abortion', 'facilities', 'operating', 'texas'], ['protest', 'vietnamese', 'magazine', 'tien', 'phong', 'stridently', 'anti', 'communist', 'views', 'protest', 'nguyen', 'thanh', 'hoang', 'criticizing', 'united', 'front', 'liberation', 'vietnam'], ['target', 'livestock', 'industry', 'causes', 'irreparable', 'damage', 'environment'], ['protest', 'united', 'states', 'based', 'businesses', 'send', 'supplies', 'cuba'], ['protest', 'irs', 'taxation', 'system', 'united', 'states', 'government'], ['protest', 'logging', 'practices'], ['protest', 'companies', 'promoting', 'travel', 'cuba'], ['protest', 'practice', 'abortion', 'sabotage', 'abortion', 'facilities', 'operating', 'florida'], ['protest', 'use', 'animals', 'laboratory', 'testing'], ['protest', 'practice', 'abortion', 'sabotage', 'abortion', 'facilities', 'operating', 'florida'], ['protest', 'practice', 'abortion', 'sabotage', 'abortion', 'facilities', 'operating', 'florida'], ['opposition', 'towards', 'lam', 'ton', 'public', 'call', 'vietnamese', 'community', 'us', 'negotiate', 'political', 'social', 'issues', 'socialist', 'republic', 'vietnam'], ['alf', 'fire', 'set', 'another', 'strike', 'campaign', 'industry', 'slaughters', 'billions', 'animals', 'poisons', 'people', 'world', 'deadly', 'chemicals', 'bad', 'nutrition'], ['name', 'animal', 'rights'], ['protest', 'logging'], ['group', 'responsible', 'multiple', 'incidents', 'sabotage', 'power', 'lines', 'ski', 'equipment', 'additionally', 'particular', 'preparation', 'planned', 'nuclear', 'facilities', 'never', 'came', 'fruition', 'due', 'arrests'], ['protest', 'practice', 'abortion', 'sabotage', 'abortion', 'facilities', 'operating', 'new', 'hampshire'], [], [], ['protest', 'reapproachment', 'vietnam', 'united', 'states', 'specifically', 'provoked', 'due', 'mistranslated', 'article', 'doan', 'van', 'toai', 'wrote', 'english', 'language', 'policy', 'newspaper'], ['though', 'motivations', 'seem', 'racial', 'undertones', 'trial', 'proceedings', 'revealed', 'moody', 'vendetta', 'court', 'system', 'denying', 'appeal', 'upholding', 'conviction', 'possessing', 'pipe', 'bombs', 'additionally', 'moody', 'believed', 'blacks', 'received', 'preferential', 'treatment', 'courts'], ['protest', 'perceived', 'supporters', 'vietnam', 'communist', 'government', 'within', 'american', 'vietnamese', 'community'], ['protest', 'practice', 'abortion', 'sabotage', 'abortion', 'facilities', 'operating', 'michigan'], ['protest', 'practice', 'abortion', 'sabotage', 'abortion', 'facilities', 'operating', 'pennsylvania'], ['protest', 'cubans', 'miami', 'supported', 'increased', 'relations', 'fidel', 'castro', 'regime'], ['protest', 'practice', 'abortion', 'sabotage', 'abortion', 'facilities', 'operating', 'new', 'jersey'], ['protest', 'vietnamese', 'magazine', 'tien', 'phong', 'stridently', 'anti', 'communist', 'views'], ['lepine', 'women', 'killed', 'feminists', 'hated', 'feminists', 'suicide', 'note', 'left', 'scene', 'reflected', 'personal', 'ideological', 'misogyny'], ['protest', 'th', 'circuit', 'court', 'appeals', 'prioritizing', 'civil', 'rights', 'african', 'americans', 'creating', 'climate', 'led', 'numerous', 'acts', 'african', 'american', 'males', 'white', 'women', 'authorities', 'moody', 'attempting', 'get', 'even', 'courts', 'found', 'guilty', 'conviction', 'possessing', 'pipe', 'bomb', 'th', 'circuit', 'court', 'refused', 'overturn', 'conviction'], ['protest', 'practice', 'abortion', 'sabotage', 'planned', 'parenthood', 'operating', 'kansas', 'city', 'area'], ['protest', 'united', 'states', 'presence', 'puerto', 'rico'], ['protest', 'united', 'states', 'presence', 'puerto', 'rico'], ['khalifa', 'believed', 'islam', 'based', 'numerology', 'put', 'forth', 'interpretations', 'quran', 'opposed', 'fundamentalists'], [], ['midst', 'several', 'fire', 'bombings', 'mosques', 'west', 'coast'], ['protest', 'pacific', 'gas', 'electric', 'company'], ['order', 'give', 'attention', 'earth', 'day', 'protest'], ['groups', 'area', 'toppling', 'sawing', 'power', 'poles', 'past', 'days', 'protest', 'pacific', 'gas', 'electric', 'company'], ['protest', 'practice', 'abortion', 'sabotage', 'abortion', 'facilities', 'operating', 'oregon'], ['protest', 'practice', 'abortion', 'sabotage', 'abortion', 'facilities', 'operating', 'new', 'york', 'moreover', 'dinicola', 'furious', 'supreme', 'court', 'decision', 'banning', 'protesters', 'blocking', 'abortion', 'clinics'], ['intimidate', 'jews'], ['protest', 'practice', 'abortion', 'sabotage', 'abortion', 'facilities', 'operating', 'california'], [], ['indicate', 'battle', 'military', 'liberation', 'tigers', 'tamil', 'eelam', 'ltte', 'territory'], ['protest', 'practice', 'abortion', 'sabotage', 'abortion', 'facilities', 'operating', 'california'], [], ['protest', 'practice', 'abortion', 'sabotage', 'abortion', 'facilities', 'operating', 'washington'], [], ['protest', 'practice', 'abortion', 'sabotage', 'abortion', 'facilities', 'operating', 'massachusetts'], [], ['protest', 'deployment', 'puerto', 'rican', 'soldiers', 'united', 'students', 'army', 'persian', 'gulf', 'war', 'denounce', 'plebiscite', 'determine', 'political', 'status', 'puerto', 'rico'], ['protest', 'deployment', 'puerto', 'rican', 'soldiers', 'united', 'students', 'army', 'persian', 'gulf', 'war', 'denounce', 'plebiscite', 'determine', 'political', 'status', 'puerto', 'rico'], ['protest', 'vietnamese', 'magazine', 'tien', 'phong', 'stridently', 'anti', 'communist', 'views', 'protest', 'triet', 'le', 'criticizing', 'united', 'front', 'liberation', 'vietnam'], [], ['protest', 'practice', 'abortion', 'sabotage', 'abortion', 'facilities', 'operating', 'indiana'], [], [], [], ['ira', 'campaign', 'drive', 'british', 'northern', 'irelandthe', 'group', 'draws', 'support', 'working', 'class', 'catholic', 'minority', 'discriminated', 'protestant', 'fighting', 'unified', 'independent', 'ireland'], ['protest', 'practice', 'abortion', 'sabotage', 'abortion', 'facilities', 'operating', 'ohio'], ['jean', 'claude', 'olivier', 'supporter', 'jean', 'bertrand', 'aristide', 'presidency', 'vocal', 'critic', 'haitian', 'organizations', 'democracy', 'specifically', 'assassinated', 'urging', 'boycott', 'concert', 'sponsored', 'louis', 'thermitus', 'thermitus', 'one', 'time', 'member', 'macoutes', 'supporter', 'military', 'junta', 'would', 'oust', 'aristide'], ['protest', 'practice', 'abortion', 'sabotage', 'abortion', 'facilities', 'operating', 'ohio'], ['fritz', 'dor', 'supporter', 'jean', 'bertrand', 'aristide', 'presidency', 'vocal', 'critic', 'haitian', 'organizations', 'democracy', 'specifically', 'assassinated', 'urging', 'boycott', 'concert', 'sponsored', 'louis', 'thermitus', 'thermitus', 'one', 'time', 'member', 'macoutes', 'supporter', 'military', 'junta', 'would', 'oust', 'aristide'], ['protest', 'practice', 'abortion', 'sabotage', 'abortion', 'facilities', 'operating', 'north', 'carolina'], ['protest', 'practice', 'abortion', 'sabotage', 'abortion', 'facilities', 'operating', 'north', 'carolina'], [], ['protest', 'irs', 'taxation', 'system', 'united', 'states', 'government'], ['speculate', 'intended', 'counter', 'losses', 'suffered', 'rebels', 'army', 'operations', 'north'], ['protest', 'practice', 'abortion', 'sabotage', 'abortion', 'facilities', 'operating', 'ohio'], ['protest', 'practice', 'abortion', 'sabotage', 'abortion', 'facilities', 'operating', 'new', 'jersey'], [], ['protest', 'practice', 'abortion', 'sabotage', 'abortion', 'facilities', 'operating', 'alabama'], ['protest', 'fur', 'industry'], ['protest', 'fur', 'industry'], [], ['protest', 'practice', 'abortion', 'sabotage', 'abortion', 'facilities', 'operating', 'north', 'carolina'], [], ['protest', 'practice', 'abortion', 'instill', 'fear', 'physicians', 'performed', 'abortions', 'specifically', 'retribution', 'dr', 'carhart', 'crimes', 'humanity'], [], ['protest', 'practice', 'abortion', 'sabotage', 'abortion', 'facilities', 'operating', 'illinois', 'specifically', 'facilities', 'perform', 'abortions', 'second', 'trimester', 'pregnancy'], ['protest', 'practice', 'abortion', 'doctors', 'performed', 'abortions', 'florida'], ['passengers', 'fact', 'finding', 'mission', 'strategy', 'defuse', 'hostilities', 'area'], ['discourage', 'raising', 'animals', 'fur', 'trade'], ['protest', 'practice', 'abortion', 'instill', 'fear', 'physicians', 'performed', 'abortions'], ['protest', 'practice', 'abortion', 'instill', 'fear', 'physicians', 'perform', 'abortions'], ['protest', 'practice', 'abortion', 'sabotage', 'abortion', 'facilities', 'operating', 'montana'], ['protest', 'practice', 'abortion', 'sabotage', 'abortion', 'facilities', 'operating', 'texas'], ['discourage', 'raising', 'animals', 'fur', 'trade'], ['mujahideen', 'khalq', 'group', 'opposed', 'iranian', 'regime', 'time', 'seized', 'iranian', 'mission', 'protest', 'bombing', 'base', 'military', 'wing', 'group', 'stationed', 'iraq'], ['protest', 'practice', 'abortion', 'sabotage', 'abortion', 'facilities', 'operating', 'oregon'], ['protest', 'irs', 'taxation', 'system', 'united', 'states', 'government'], ['protest', 'practice', 'abortion', 'sabotage', 'abortion', 'facilities', 'operating', 'north', 'dakota'], ['protest', 'practice', 'abortion', 'sabotage', 'abortion', 'facilities', 'operating', 'california'], ['protest', 'practice', 'abortion', 'sabotage', 'abortion', 'facilities', 'operating', 'missouri'], ['protest', 'practice', 'abortion', 'sabotage', 'abortion', 'facilities', 'operating', 'california'], ['protest', 'practice', 'abortion', 'sabotage', 'abortion', 'facilities', 'operating', 'oregon'], ['two', 'victims', 'known', 'actions', 'liberation', 'tigers', 'tamil', 'eelam'], ['protest', 'practice', 'abortion', 'sabotage', 'abortion', 'facilities', 'operating', 'nevada'], ['protest', 'practice', 'abortion', 'sabotage', 'abortion', 'facilities', 'operating', 'california'], ['protest', 'practice', 'abortion', 'sabotage', 'abortion', 'facilities', 'operating', 'nevada'], ['protest', 'practice', 'abortion', 'sabotage', 'abortion', 'facilities', 'operating', 'virginia'], ['protest', 'practice', 'abortion', 'sabotage', 'abortion', 'facilities', 'operating', 'oregon'], [], ['protest', 'fur', 'industry'], ['protest', 'fur', 'industry'], ['protest', 'practice', 'abortion', 'sabotage', 'abortion', 'facilities', 'operating', 'illinois'], ['protest', 'animal', 'suffering'], ['protest', 'united', 'states', 'hegemony', 'latin', 'america', 'exploitation', 'indigenous', 'people'], ['protest', 'practice', 'abortion', 'sabotage', 'abortion', 'facilities', 'operating', 'california'], ['fourth', 'reich', 'skinheads', 'hoped', 'ignite', 'race', 'war'], ['promote', 'puerto', 'rican', 'independence', 'united', 'states', 'call', 'release', 'puerto', 'rican', 'political', 'prisoners', 'united', 'states', 'jails'], ['protest', 'practice', 'abortion', 'sabotage', 'abortion', 'facilities', 'operating', 'california'], ['protest', 'practice', 'abortion', 'sabotage', 'abortion', 'facilities', 'operating', 'montana'], ['criticize', 'government', 'israel', 'left', 'wing', 'jewish', 'organizations'], ['criticize', 'government', 'israel', 'left', 'wing', 'jewish', 'organizations'], ['aryan', 'republican', 'army', 'robbed', 'banks', 'simultaneously', 'dispose', 'state', 'capital', 'provide', 'logistical', 'support', 'revolutionary', 'war', 'overthrow', 'united', 'states', 'government', 'money', 'bank', 'robberies', 'used', 'purchase', 'guns', 'vehicles', 'safehouses', 'portion', 'money', 'donated', 'various', 'right', 'wing', 'extremist', 'causes'], ['generally', 'animal', 'rights', 'slogan', 'meat', 'murder', 'painted', 'trucks'], ['generally', 'animal', 'rights', 'slogan', 'meat', 'murder', 'painted', 'building'], ['aryan', 'republican', 'army', 'robbed', 'banks', 'simultaneously', 'dispose', 'state', 'capital', 'provide', 'logistical', 'support', 'revolutionary', 'war', 'overthrow', 'united', 'states', 'government', 'money', 'bank', 'robberies', 'used', 'purchase', 'guns', 'vehicles', 'safehouses', 'portion', 'money', 'donated', 'various', 'right', 'wing', 'extremist', 'causes'], ['aryan', 'republican', 'army', 'robbed', 'banks', 'simultaneously', 'dispose', 'state', 'capital', 'provide', 'logistical', 'support', 'revolutionary', 'war', 'overthrow', 'united', 'states', 'government', 'money', 'bank', 'robberies', 'used', 'purchase', 'guns', 'vehicles', 'safehouses', 'portion', 'money', 'donated', 'various', 'right', 'wing', 'extremist', 'causes'], ['aryan', 'republican', 'army', 'robbed', 'banks', 'simultaneously', 'dispose', 'state', 'capital', 'provide', 'logistical', 'support', 'revolutionary', 'war', 'overthrow', 'united', 'states', 'government', 'money', 'bank', 'robberies', 'used', 'purchase', 'guns', 'vehicles', 'safehouses', 'portion', 'money', 'donated', 'various', 'right', 'wing', 'extremist', 'causes'], ['protect', 'planet', 'earth', 'loggers'], ...]
from pprint import pprint
# number of topics
num_topics = 10
# Build LDA model
lda_model = gensim.models.LdaMulticore(corpus=corpus,
id2word=id2word,
num_topics=num_topics)
# Print the Keyword in the 10 topics
pprint(lda_model.print_topics())
doc_lda = lda_model[corpus]
[(0, '0.030*"bangladesh" + 0.022*"january" + 0.015*"victim" + 0.014*"elections" + ' '0.012*"retaliation" + 0.011*"day" + 0.010*"october" + 0.010*"began" + ' '0.010*"police" + 0.009*"law"'), (1, '0.018*"abortion" + 0.015*"protest" + 0.014*"nepal" + 0.012*"sabotage" + ' '0.012*"party" + 0.012*"government" + 0.009*"facilities" + 0.009*"operating" ' '+ 0.009*"practice" + 0.008*"area"'), (2, '0.046*"security" + 0.039*"forces" + 0.035*"islamic" + 0.029*"campaign" + ' '0.028*"government" + 0.025*"extremists" + 0.022*"destabilize" + ' '0.022*"algeria" + 0.018*"algerian" + 0.014*"apostate"'), (3, '0.024*"shiite" + 0.023*"elections" + 0.021*"sunni" + 0.017*"nationwide" + ' '0.015*"communities" + 0.014*"began" + 0.014*"scheduled" + 0.014*"sectarian" ' '+ 0.012*"area" + 0.012*"retaliation"'), (4, '0.032*"levant" + 0.022*"victims" + 0.022*"people" + 0.015*"army" + ' '0.014*"response" + 0.012*"accused" + 0.011*"group" + 0.010*"victim" + ' '0.009*"national" + 0.009*"state"'), (5, '0.017*"members" + 0.016*"state" + 0.015*"victims" + 0.012*"islamic" + ' '0.011*"retaliation" + 0.010*"group" + 0.010*"military" + 0.009*"government" ' '+ 0.009*"iraq" + 0.008*"qa"'), (6, '0.021*"retaliation" + 0.018*"army" + 0.018*"npa" + 0.017*"military" + ' '0.015*"order" + 0.015*"people" + 0.014*"new" + 0.014*"taliban" + ' '0.012*"maoist" + 0.009*"day"'), (7, '0.034*"islamic" + 0.023*"shiite" + 0.020*"communities" + 0.017*"pakistan" + ' '0.016*"sectarian" + 0.016*"victims" + 0.015*"state" + 0.014*"police" + ' '0.014*"sunni" + 0.012*"maoist"'), (8, '0.022*"police" + 0.021*"minority" + 0.019*"iraq" + 0.018*"group" + ' '0.016*"maoist" + 0.015*"government" + 0.014*"extortion" + 0.013*"informant" ' '+ 0.010*"maoists" + 0.009*"scheduled"'), (9, '0.017*"cpi" + 0.015*"government" + 0.014*"military" + 0.014*"demand" + ' '0.014*"iraq" + 0.010*"islamic" + 0.009*"people" + 0.009*"individual" + ' '0.009*"state" + 0.008*"united"')]
import pyLDAvis.gensim_models
import pickle
import pyLDAvis
import os
# Visualize the topics
pyLDAvis.enable_notebook()
LDAvis_prepared = pyLDAvis.gensim_models.prepare(lda_model, corpus, id2word)
LDAvis_prepared
topic_list = []
for i in range(len(corpus)):
tmp = lda_model.get_document_topics(corpus[i])
max_num = 0
max_j = 0
#print(tmp, end = '')
for j in range(len(tmp)):
if tmp[j][1] > max_num:
max_num = tmp[j][1]
max_j = j
topic_list.append(tmp[max_j][0])
#print(i, tmp[max_j])
gtd["motivation_topic"] = topic_list
gtd.head()
targ_list = gtd.targtype1_txt.unique()
d = np.random.random(((len(targ_list),10)))
for i in range(len(targ_list)):
for j in range(10):
d[i][j] = len(gtd[(gtd['targtype1_txt'] == targ_list[i]) & (gtd['motivation_topic'] == j)])/len(gtd[(gtd['motivation_topic'] == j)])
fig = plt.figure(figsize = (20,11), dpi = 150)
ax = fig.add_subplot(title = 'Motivation vs Target Heatmap', xlabel = 'Motivation Topic', ylabel = 'Target Type')
ax.set_yticks(np.arange(len(targ_list)))
ax.set_xticks(np.arange(10))
ax.set_yticklabels(targ_list)
#ax.set_yticklabels(atk_list)
ax.imshow(d)
plt.show()
e = np.random.random(((len(wep_list),10)))
for i in range(len(wep_list)):
for j in range(10):
e[i][j] = len(gtd[(gtd['weaptype1_txt'] == wep_list[i]) & (gtd['motivation_topic'] == j)])
fig = plt.figure(figsize = (10,5.5), dpi = 150)
ax = fig.add_subplot(title = 'Motivation vs Weapon Selection Heatmap', xlabel = 'Motivation Topic', ylabel = 'Target Type')
ax.set_yticks(np.arange(len(wep_list)))
ax.set_xticks(np.arange(10))
ax.set_yticklabels(wep_list_label)
#ax.set_yticklabels(atk_list)
ax.imshow(e)
plt.show()